Quatrain

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Quatrain(Question Answering for Patch Correctness Evaluation), a supervised learning approach that exploits a deep NLP model to classify the relatedness of a bug report with a patch description.

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[![DOI](https://zenodo.org/badge/519884433.svg)](https://zenodo.org/badge/latestdoi/519884433)

# Is this Change the Answer to that Problem? Correlating Descriptions of Bug and Code Changes for Evaluating Patch Correctness
```bibtex
@inproceedings{tian2022change,
  title={Is this Change the Answer to that Problem? Correlating Descriptions of Bug and Code Changes for Evaluating Patch Correctness},
  author={Tian, Haoye and Tang, Xunzhu and Habib, Andrew and Wang, Shangwen and Liu, Kui and Xia, Xin and Klein, Jacques and Bissyand{\'E}, Tegawend{\'E} F},
  booktitle={37th IEEE/ACM International Conference on Automated Software Engineering},
  pages={1--13},
  url = {https://doi.org/10.1145/3551349.3556914}, 
  doi = {10.1145/3551349.3556914},
  year={2022}
}
```
Paper Link: https://dl.acm.org/doi/abs/10.1145/3551349.3556914

Quatrain
=======
Quatrain (Question Answering for Patch Correctness Evaluation), a supervised learning approach that exploits a deep NLP model to classify the
relatedness of a bug report with a patch description.

### Catalogue of Repository
```
artifact_detection_model: a model to detect codes in text.
data: processed and structured dataset.
experiment: scripts to obtain experimental results of paper. 
figure: saved figures for experiment
preprocess: scripts to extract bug reports and commit messages.
representation: embeddings representation model.
utils: scripts to deduplicate dataset.
---------------
INSTALL.md: installation instructions.
quatrain_model.h5: pre-trained QUATRAIN model for users' custom prediction.
requirements.txt: required dependencies.
run.py: entrance to conduct experiment.
```
## Ⅰ) Dataset
  1. bug report summary: title for bug issue.
  2. bug report description: detailed description for bug issue.
  3. patch description: CodeTrans-generated commit message for patch.

### A) Table 1: Datasets of labelled patches.
* [data/bugreport_patch.txt](https://github.com/Trustworthy-Software/Quatrain/blob/main/data/bugreport_patch.txt): 9135 (1591:7544) Pairs of Bug report & Commit message. Structured as `bug-id $$ bug report summary $$ bug report description $$ patchId $$ patch description $$ label`
* [data/bugreport_patch_json_bert.pickle](https://github.com/Trustworthy-Software/Quatrain/blob/main/data/bugreport_patch_json_bert.pickle): Bert embeddings of Pairs of Bug report & Commit message.
* data/bugreport_patch_array_bert.pickle: Bert embeddings of paris for 10-fold cross validation.

### B) Colleted elements
```
data/BugReport: Bug reports texts for Defects4j, Bugsjar, Bears. Structured as `bug-id $$ bug report summary $$ bug report description` in txt file.
data/CommitMessage: Commit messages written by developer or generated by CodeTrans in format of json and pickle. Structured as `bug-id: commit message` in json file.
---------------
BATS_RESULT_0.0.json: the prediction results of BATS with cut-off 0.0 on our dataset. 
BATS_RESULT_0.8.json: the prediction results of BATS with cut-off 0.8 on our dataset.
PATCHSIM_RESULT.json: the prediction results of Patch-Sim on our dataset.
PatchLabelsYe.csv: the original prediction results of ODS.
Bears_testinfo.txt: the stack failure information of test suites for Bears.   
bears_index_dict(inverse).json: dictionary of bug-id and commit-id. 
save_bugreport_patch.py: script to produce data/bugreport_patch.txt.
```

## Ⅱ) Requirements
### A) Environment 
  * python 3.7 (Anaconda recommended)
  * pip install -r requirements.txt

run `sudo apt-get install python3.7-dev` first if you don't have python3.7 dev package.

### B) Data elements 
  download _ASE2022withTextUnique.zip_ (need to be unzipped) and _ASE_features2_bert.pickle_ from [data in Zenodo](https://zenodo.org/record/7029808#.YwuKF2QzZb8 "Dataset for Quatrain"), 
  accordingly change the absolute path of these two files in **experiment/config.py** of this repository as below.
  1. self.path_patch ---> ASE2022withTextUnique.  Original dataset with patches text and commit messages text.
  2. self.path_ASE2020_feature ---> ASE_features2_bert.pickle. The feature from Tian et al.'s ASE2020 [paper](https://ieeexplore.ieee.org/abstract/document/9286101) for our RQ3 DL experiment. 
  
Simplified dataset: [ASE2022withText.](https://zenodo.org/record/7315302#.Y293X2RKhhE)

## Ⅲ) Experiment

To obtain the experimental results of our paper, execute `run.py` with the following parameters:

### A) Sec. 2.2 (Hypothesis validation)
  1. **Figure 2:** Distributions of Euclidean distances between bug and patch descriptions.
```
python run.py hypothesis
```

### B) Sec. 5.1 (RQ1: Effectiveness of Quatrain) 
  1. **Figure 5:** Distribution of Patches in Train and Test Data. 
  2. **Table 2:** Confusion matrix of Quatrain prediction.
```
python run.py RQ1
```
  3. **The improved F1:** a better F1 score of 0.793 by re-balancing the test data.
```
python run.py RQ1 balance
```

### C) Sec. 5.2 (RQ2: Analysis of the Impact of Input Quality on Quatrain)
#### RQ 2.1
  1. **Figure 6:**  Impact of length of patch description to prediction.
```
python run.py RQ2.1
```
#### RQ 2.2
  2. **Figure 7:**  The distribution of probability of patch correctness
on original and random bug report.
  3. **The dropped +Recall:**  22% (241/1073) of developer patches, which were previously predicted as correct, are no longer recalled by Quatrain after they have been associated to a random bug report.
```
python run.py RQ2.2
```
#### RQ 2.3
  4. **Figure 8:**   Impact of distance between generated patch descrip-
tion to ground truth on prediction performance.
  5. **The dropped +Recall:**  The metric (+Recall) drops by 37 percentage points to 45\% when the developer-written descriptions are replaced with CodeTrans-generated descriptions.
```
python run.py RQ2.3
```
  6. **The dropped AUC:**  we evaluated Quatrain in a setting where all developer commit messages were replaced with CodeTrans-generated descriptions: the AUC metric dropped by 11 percentage points to 0.774, confirming our findings.
```
python run.py RQ2.3 CodeTrans
```

### D) Sec. 5.3 (RQ3: Comparison Against the State of the Art)
#### Sec. 5.3.1 (Comparing against Static Approaches)
  1. **Table 3:** Quatrain vs a DL-based patch classifie.
  2. **New identification:**  Among 9135 patches, our approach identifies 7842 patches, of which 2735 patches cannot be identified by Tian et al.'s approach (RF).
```
python run.py RQ3 DL
```
  3. **Table 4:** Quatrain vs BATS.
  4. **New identification:**  180 out of 345 patches are exclusively identified by Quatrain.
```
python run.py RQ3 BATS
```

#### Sec. 5.3.2 (Comparing against Dynamic Approach)
  1. **Table 5:** Quatrain vs (execution-based) PATCH-SIM.
  2. **New identification:**  Most of the patches (1856/3149) that we identify are not correctly predicted by PATCH-SIM.
```
python run.py RQ3 PATCHSIM
```

### E) Sec. 6.1 (Experimental insights)
  1. **RF with 10-fold:** RandomForest (RF) on the embeddings of the bug report and the patch based on 10-fold cross validation.
  2. **RF with 10-group:** RandomForest (RF) on the embeddings of the bug report and the patch based on 10-group cross validation.
```
python run.py insights
```

## Ⅳ) Custom Prediction
To predict the correctness of your custom patches, you are welcome to use the prediction interface.
### A) Requirements for BERT
  * **BERT model client&server:** 24-layer, 1024-hidden, 16-heads, 340M parameters. download it [here](https://storage.googleapis.com/bert_models/2019_05_30/wwm_cased_L-24_H-1024_A-16.zip).
  * **Environment for BERT server** (different from reproduction)
    * python 3.7 
    * pip install tensorflow==1.14
    * pip install bert-serving-client==1.10.0
    * pip install bert-serving-server==1.10.0
    * pip install protobuf==3.20.1
    * Launch BERT server via `bert-serving-start -model_dir "Path2BertModel"/wwm_cased_L-24_H-1024_A-16 -num_worker=2 -max_seq_len=360 -port 8190`
    * switch the port in [BERT_Port](https://github.com/Trustworthy-Software/Quatrain/blob/main/representation/word2vec.py#L42) in case your port 8190 is occupied.
  * **Bug report text:** developer-written bug report.
  * **Patch description text:** generating patch description for your plausible patches with commit message generation tools, e.g. CodeTrans. [Github](https://github.com/agemagician/CodeTrans) and [API](https://huggingface.co/SEBIS/code_trans_t5_large_commit_generation_transfer_learning_finetune).

### B) Predict
Let's give it a try!
```
python run.py predict $bug_report_text $patch_description_text
```
For instance: `python run.py predict 'Missing type-checks for var_args notation' 'check var_args properly'`

## Ⅴ) Custom Train
To re-train QUATRAIN model on our or other dataset, execute the following steps.
  1. Structure your dataset as [data/bugreport_patch.txt](https://github.com/Trustworthy-Software/Quatrain/blob/main/data/bugreport_patch.txt). 
  2. Obtain Bert embeddings of your dataset via `experiment/save_bugreport_dataset_json.py`
  3. Accordingly, change self.dataset_json in experiment/config.py 
  4. Execute `python run.py RQ1`

## License
Quatrain is distributed under the terms of the MIT License, see [LICENSE](LICENSE).

[//]: # (### deduplicate.py)
[//]: # (deduplicating same patches.)

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